A fast centrality-meter for heavy-ion collisions at the CBM experiment

Kuttan, Manjunath Omana; Steinheimer, Jan; Zhou, Kai; Redelbach, A.; Stoecker, Horst

doi:10.1016/j.physletb.2020.135872

Cited by 42 publications

(43 citation statements)

References 32 publications

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“…For events below 2 fm, the simple model had a relative precision ((σ pred / true b)× 100%) reaching up to 200% while this was less than 80% for the DL models with the values being less than 50% for events with 1 fm or above. Moreover, in Figure 5 in [38], it was also shown that the DL models are robust to small changes in the physics of the underlying event generator model.…”

Section: Resultsmentioning

confidence: 91%

“…Figures 2-4 in [38] illustrate the performance of the PointNet models in comparison to a simple polynomial fit (Polyfit) to the track multiplicity vs. impact parameter relation. The DL models were shown to be more accurate and precise than the Polyfit model.…”

Section: Resultsmentioning

confidence: 99%

“…In this proceedings, we summarise the results and performance of PointNet based Deep Learning models for impact parameter determination. A detailed description of the models and discussions on their performance can be found in [38].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Impact Parameter Determination for the CBM Experiment

et al. 2021

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In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

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Deep Learning Based Impact Parameter Determination for the CBM Experiment

et al. 2021

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“…After the step of event building separate events are technically defined and can be processed, also in the approach of this analysis. It is interesting to note that the process of event building might also be improved by DL-based methods, similar to the PointNet recently developed in [33].…”

Section: Microscopic and Macroscopic Dynamical Models Used To Generate The Datamentioning

confidence: 99%

“…DL models can be trained on point cloud data using the PointNet [70] architecture. PointNet based DL models have been shown to learn from heavy ion collision data to reconstruct the impact parameter of collisions in [33,71]. In this study, we used a similar network architecture but less complex (i.e.…”

Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan

Zhou

Steinheimer

et al. 2021

J. High Energ. Phys.

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A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.

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High-energy nuclear physics meets machine learning

Pang

et al. 2023

NUCL SCI TECH

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Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning (ML) have begun to merge in the last few years, yielding interesting results. It is worthy to raise the profile of utilizing this novel mindset from ML in HENP, to help interested readers see the breadth of activities around this intersection. The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP. From different aspects and using examples, we examine how scientific questions involving HENP can be answered using ML.

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A fast centrality-meter for heavy-ion collisions at the CBM experiment

Cited by 42 publications

References 32 publications

Deep Learning Based Impact Parameter Determination for the CBM Experiment

Deep Learning Based Impact Parameter Determination for the CBM Experiment

An equation-of-state-meter for CBM using PointNet

High-energy nuclear physics meets machine learning

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